Financial data fundamentals
Working with OHLCV data, handling missing values, identifying lookahead bias — the issues that break models before training even starts.
Vephoxar Lre — Learning Program
Machine Learning in Finance — a structured path through real methods
This program covers the practical mechanics of applying ML to financial data — from feature engineering on time series to model evaluation in production-like conditions. No shortcuts, no hype. Participants work through assignments that reflect actual problems analysts and quant teams encounter.
What the program covers, week by week
Six modules, each building on the previous. The first two weeks are deliberately slow — participants spend time understanding data quality issues in financial datasets before touching any model. Weeks three through six introduce supervised learning, cross-validation strategies specific to time-ordered data, and interpretability tools like SHAP. Every module ends with a graded assignment using real market data.
Feature construction and selection
Building lag features, rolling statistics, and domain-informed signals. Understanding which features add information versus which inflate apparent performance.
Supervised learning applied to markets
Decision trees, gradient boosting, and linear models tested on classification and regression tasks — with financial framing, not toy datasets.
Validation without data leakage
Walk-forward validation, purging and embargo techniques. Standard k-fold fails on time series — this module explains exactly why and what to do instead.
Model interpretability
SHAP values, permutation importance, partial dependence plots. Understanding what a model actually learned matters as much as its accuracy score.
Deployment and monitoring basics
Packaging a model for repeated use, tracking prediction drift, and knowing when to retrain. A working pipeline, not just a notebook.
After six weeks — what participants actually walk away with
Concrete skills take time to form. This program is designed for people who can commit around eight hours per week. Those who do tend to finish with a working project they can discuss in professional settings.
A completed end-to-end pipeline
Participants build and submit a full ML pipeline — data ingestion through prediction output — using a dataset of their choice from a curated list. The final assignment is reviewed by a program instructor with written feedback.
Practical fluency with Python tooling
scikit-learn, pandas, SHAP, and matplotlib are used throughout. Not as isolated exercises — as tools inside a coherent workflow. By module four most participants stop looking things up and start adapting code on their own.
The ability to read and critique ML work
Knowing what questions to ask about a model — how was it validated, what features dominate, does the performance hold out-of-sample — is as useful as building one. The program trains this critical reading alongside technical construction.